PERBANDINGAN ESTIMATOR CENSORED LEAST ABSOLUTE DEVIATIONS (CLAD) DAN SYMMETRICALLY CENSORED LEAST SQUARES (SCLS) UNTUK MODEL REGRESI TOBIT (Studi Kasus : Analisis Faktor-Faktor yang Mempengaruhi Partisipasi Perempuan dalam Perekonomian Rumah Tangga di Provinsi Daerah Istimewa Yogyakarta)
This graduating paper discusses alternatives for maximum likelihood estimation of the censored regression or censored �Tobit� model. There are two alternative methods that will be discusses and compared: Censored Least Absolute Deviations (CLAD) and Symmetrically Censored Least Absolute Deviatio...
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| Main Authors: | , |
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| Format: | Theses and Dissertations NonPeerReviewed |
| Published: |
[Yogyakarta] : Universitas Gadjah Mada
2014
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| Subjects: | |
| Online Access: | https://repository.ugm.ac.id/132176/ http://etd.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=72696 |
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| Institution: | Universitas Gadjah Mada |
| Summary: | This graduating paper discusses alternatives for maximum likelihood estimation
of the censored regression or censored �Tobit� model. There are two alternative
methods that will be discusses and compared: Censored Least Absolute
Deviations (CLAD) and Symmetrically Censored Least Absolute Deviations
(SCLS). Unlike maximum likelihood estimator, CLAD is consistent and
asymptotically normal for a wide class of error distributions and robust to
heterokedasticity. Meanwhile, SCLS is not completely general, since it is based
upon the assumption of symmetrically (and independently) distributed error
terms. However this estimator will be consistent even though the residuals are not
identically distributed and remain robust to heterokedasticity data.
In this case study, the researcher use National Labor Force Survey Data 2013 to
examine factors that influence women�s participant in domestic economy of
Yogyakarta province. |
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